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code demonstrates a practical approach to evaluating and refining a text generation model for specific tasks. Starting with an evaluation process, we generate model outputs based on a structured prompt and analyze the quality of these outputs. By setting up evaluation metrics such as accuracy or relevance, we can systematically identify areas where the model performs well and where it may require improvement.\n","\n","Through this process, we aim not only to assess the model’s current performance but also to gather insights that can guide targeted improvements. By iteratively refining the prompt, adjusting parameters, or even retraining on relevant datasets, we create a feedback loop. This loop continuously enhances the model’s ability to generate relevant, structured responses, bridging the gap between evaluation and ongoing optimization.\n","\n","With each cycle of evaluation and refinement, this approach drives the model toward better alignment with specific goals, such as generating accurate and contextually appropriate responses for complex topics like blockchain in supply chain managem"],"metadata":{"id":"Ld2OXccPJ1bC"}},{"cell_type":"markdown","source":["### First of all let's install required packages"],"metadata":{"id":"9l0_QeSWJ2_m"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G9W8Qt-6I36E","executionInfo":{"status":"ok","timestamp":1730488659986,"user_tz":0,"elapsed":15444,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"edd4faa9-5ced-4b7d-d62d-fbffb7a3abff"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: 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The pipeline function simplifies the process of setting up a model for specific tasks, while set_seed ensures consistent outputs across runs by fixing the randomness in model generation."],"metadata":{"id":"VRYRu7XfNFqr"}},{"cell_type":"code","source":["# Step 1: Import necessary libraries from the Hugging Face Transformers library\n","from transformers import pipeline, set_seed"],"metadata":{"id":"3BExBpJWKRbp","executionInfo":{"status":"ok","timestamp":1730489452560,"user_tz":0,"elapsed":25785,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["This initializes a text-generation pipeline with GPT-Neo 1.3B, a model by EleutherAI. This model can handle complex prompts and generate human-like responses. Here, \"text-generation\" specifies the type of task for the pipeline, and the model name \"EleutherAI/gpt-neo-1.3B\" tells the pipeline which model to load for text generation.\n","\n","Note: GPT-Neo is open-source, making it freely available and a good alternative to proprietary models like OpenAI’s GPT-3."],"metadata":{"id":"3B4zMfgaNTEl"}},{"cell_type":"code","source":["# Step 2: Initialize a text-generation pipeline using GPT-Neo\n","generator = pipeline(\"text-generation\", model=\"EleutherAI/gpt-neo-1.3B\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":372,"referenced_widgets":["13c5e0bbcf6843b088c4658f328a1ac2","ca91a12aba1b4c85a1e3f2a6d140ae80","e361a6e4581b4a61b878f4ce04014faf","23c332729e0a488482225d1932ee43c2","aa639b8211164b9f856b557dbdfd0540","9edfab58acbf4329b5aad04659e37cd2","030ff7d85c67479489ec991f7a5c599b","1471653deab54d14925804ff6f844bad","6cfafb581b8b4416833bda34475c934d","ecb2618e7b7d4658b071d67bbf79006d","208c24c901964d219b2ef707f067931b","e691cf8a8bea4b96a09afaf3ef3f4915","955e5d79264a4764bbf20a1854a9d06b","4d853950d2d3404f8aad9b8105bf16ca","401c72b4a6d2428a8299f3edb512f953","157ecd3f8f984abf98d2c34ef344878f","f8bdfefdbdd548f4bf37ae989750d020","5cf6811b29174586b0c80b59f113bec9","08aee9cc4c4545acbc5287e0b94380a3","fc8f449792914e84a90da75b32cd7c78","7d52e927e4f1436fba3152006e5fc578","12a294555bed4fe88e7d623adbfbb035","5bd12175b29f4e718f65fed4dacb8160","67bd4ae6cc3648319aa653a3e4d419d1","6dfffae8b9d945e086615530cd9d7fc1","fbd2e872747446ec8bf712f0689af2fc","67b6470f47cc4fba82e9b8860bf4ca4c","1218c40ffe5b4ac989b6e371086d4ea4","3bf32632317447eeb92148cd837c782f","5d11ec7c5b64454fa2a0c30802d62aef","00c7ceaa13874915bdafefc517ad78d1","62eeea286367451abac9ec52af0a25cc","317e7b1760374e9d823d90424f105a6c","1cf07b98d4ac41e9ba9e4013f0ff4550","7a2309a76fc94f629d31516dd8a9ea65","9af89229497d4723a50cb6f3be7b2c29","eb04b9bb5d6848a9a960034af8c20653","a0d1a19def2446ac9a4dd1b2478eb809","ced5ab377ad74937a05996f9d7b020ba","e8886efd635e4ad293e5482a14b48e9f","0fa25b00bba747bab454f3db6be4f239","5ed66e9cefbe4523923ca3b8732ffaf2","415fe2c621934c00b20682169cf6cbe0","b018907161b3409ebf94a1382ef5455d","7d292967452a4f43ac400dcdb651765a","0816bd42041c43cb98cc6ddbdbcfe9c3","66a2c4db7e214e20a6047e12e990d068","a8f9e34498fe424d9576b7481aa9fc64","ca3e1ec81bd24339956ab601c91702c4","94afe03eff344598a8525ae7e9da57ec","8350b35ad8234d018da888382c5bbc91","0d1b130568f74846ba56ff156e265310","77bda2bf142047528337d2b52054d909","9326a8ffd5ef478db521768e8ecab185","847fca1879974b818fe2dc53c52933cd","cacb0686b00f492d8d4672ccab80647a","1ba99182ab0b42d697bd6372587ddef8","ab9db98d572040458d813d02a6f93380","ac4edcd28ee24d4d8a442dc2b985a009","01ce54e568d94674b66441acd872eb7b","41eddf44737b47578e5bce5bd68ed42e","7878bea40b7d4ce99fc4012c39ac99b3","62a868952efe4b799c3378e0767e207a","e98b1d01bcb14890bb764d5f06bfb2b2","8ce885020ac44b15ac6a9e6971c676bf","cbb33134adf0443b817f22ed0fefcf5e"]},"id":"YEyZ1sotNSHa","executionInfo":{"status":"ok","timestamp":1730489513101,"user_tz":0,"elapsed":53927,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"15c63a26-3d91-49f6-8ebc-68c7a7c834f7"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.35k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"13c5e0bbcf6843b088c4658f328a1ac2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/5.31G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e691cf8a8bea4b96a09afaf3ef3f4915"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/200 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5bd12175b29f4e718f65fed4dacb8160"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1cf07b98d4ac41e9ba9e4013f0ff4550"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|   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It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n","  warnings.warn(\n"]}]},{"cell_type":"markdown","source":[" Setting a seed ensures that the model generates the same text for the same input every time this code is run. This is important for reproducibility in research and testing scenarios, where consistent results are needed to verify findings or refine the output."],"metadata":{"id":"Qj8e3wXYNbnx"}},{"cell_type":"code","source":["# Step 3: Set a random seed to make results reproducible\n","set_seed(42)"],"metadata":{"id":"gDPbqrpCNaB2","executionInfo":{"status":"ok","timestamp":1730489513101,"user_tz":0,"elapsed":4,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["Here, we define a prompt that guides the model to focus on specific aspects of blockchain technology (i.e., transparency, security, and traceability) and relate them to supply chain management. Well-structured prompts help the model generate focused and relevant outputs, especially in scenarios requiring technical explanations or structured responses."],"metadata":{"id":"J2W_WL3KNjod"}},{"cell_type":"code","source":["# Step 4: Define a structured prompt with clear instructions\n","prompt = \"\"\"\n","Describe the benefits of blockchain technology specifically for supply chain management.\n","Mention at least three use cases focusing on transparency, security, and traceability.\n","\"\"\""],"metadata":{"id":"NWGK9MfGNinz","executionInfo":{"status":"ok","timestamp":1730489556676,"user_tz":0,"elapsed":221,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}}},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":["The generator function is called with the prompt, instructing the model to generate text based on the specified parameters.\n","-  max_length=100: Limits the response to 100 tokens to control the response length.\n","-  num_return_sequences=1: Requests a single generated response. Adjusting this parameter can allow for multiple different responses to be generated based on the same prompt."],"metadata":{"id":"ncwCvmFvN1N4"}},{"cell_type":"code","source":["# Step 5: Generate text based on the prompt with specified parameters\n","output = generator(prompt, max_length=100, num_return_sequences=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FkpI7FXaNxyJ","executionInfo":{"status":"ok","timestamp":1730489677440,"user_tz":0,"elapsed":68984,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"26ba081d-9a3b-439a-871d-8ca0d6cbb28e"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]}]},{"cell_type":"markdown","source":["This extracts and prints the generated text from the output. The output variable holds a list of responses (since multiple sequences can be generated), and each response is a dictionary with a key \"generated_text\" containing the actual text generated by the model.\n"],"metadata":{"id":"Ym6GxIrLN_hN"}},{"cell_type":"code","source":["# Step 6: Display the generated output\n","print(output[0]['generated_text'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"grq39BawN-eU","executionInfo":{"status":"ok","timestamp":1730489677441,"user_tz":0,"elapsed":2,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"9f298021-a6a7-44fd-bbc9-63b4bfee845a"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Describe the benefits of blockchain technology specifically for supply chain management.\n","Mention at least three use cases focusing on transparency, security, and traceability.\n","Assign at least two use cases with different perspectives.\n","Identify each of the main stakeholders in the supply chain.\n","List the main stakeholders in the supply chain with the name of each of the major players.\n","Analyze and describe the supply chain process with details.\n","Describe the impact of supply chain in a particular industry\n"]}]},{"cell_type":"markdown","source":[],"metadata":{"id":"YeJrNOjTOFfc"}},{"cell_type":"code","source":[],"metadata":{"id":"2w7totBhOE9P"},"execution_count":null,"outputs":[]}]}